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ORCIDBlackboxNLP@EMNLP 2018: Brussels, Belgium
- Tal Linzen, Grzegorz Chrupala, Afra Alishahi:
Proceedings of the Workshop: Analyzing and Interpreting Neural Networks for NLP, BlackboxNLP@EMNLP 2018, Brussels, Belgium, November 1, 2018. Association for Computational Linguistics 2018, ISBN 978-1-948087-71-1 - Axel Kerinec, Chloé Braud
, Anders Søgaard:
When does deep multi-task learning work for loosely related document classification tasks? 1-8 - Zied Elloumi, Laurent Besacier, Olivier Galibert, Benjamin Lecouteux:
Analyzing Learned Representations of a Deep ASR Performance Prediction Model. 9-15 - Danilo Croce, Daniele Rossini, Roberto Basili:
Explaining non-linear Classifier Decisions within Kernel-based Deep Architectures. 16-24 - Anders Søgaard, Miryam de Lhoneux, Isabelle Augenstein:
Nightmare at test time: How punctuation prevents parsers from generalizing. 25-29 - Graham Spinks, Marie-Francine Moens:
Evaluating Textual Representations through Image Generation. 30-39 - José Camacho-Collados, Mohammad Taher Pilehvar:
On the Role of Text Preprocessing in Neural Network Architectures: An Evaluation Study on Text Categorization and Sentiment Analysis. 40-46 - Jasmijn Bastings, Marco Baroni, Jason Weston, Kyunghyun Cho, Douwe Kiela:
Jump to better conclusions: SCAN both left and right. 47-55 - Alon Jacovi, Oren Sar Shalom, Yoav Goldberg:
Understanding Convolutional Neural Networks for Text Classification. 56-65 - Ola Rønning, Daniel Hardt, Anders Søgaard:
Linguistic representations in multi-task neural networks for ellipsis resolution. 66-73 - Shun Kiyono, Sho Takase, Jun Suzuki, Naoaki Okazaki, Kentaro Inui, Masaaki Nagata:
Unsupervised Token-wise Alignment to Improve Interpretation of Encoder-Decoder Models. 74-81 - Madhumita Sushil, Simon Suster, Walter Daelemans:
Rule induction for global explanation of trained models. 82-97 - Shauli Ravfogel, Yoav Goldberg, Francis M. Tyers:
Can LSTM Learn to Capture Agreement? The Case of Basque. 98-107 - João Loula, Marco Baroni, Brenden M. Lake:
Rearranging the Familiar: Testing Compositional Generalization in Recurrent Networks. 108-114 - Luzi Sennhauser, Robert C. Berwick:
Evaluating the Ability of LSTMs to Learn Context-Free Grammars. 115-124 - Reid Pryzant, Sugato Basu, Kazoo Sone:
Interpretable Neural Architectures for Attributing an Ad's Performance to its Writing Style. 125-135 - Eric Wallace, Shi Feng, Jordan L. Boyd-Graber:
Interpreting Neural Networks with Nearest Neighbors. 136-144 - Yova Kementchedjhieva, Adam Lopez:
'Indicatements' that character language models learn English morpho-syntactic units and regularities. 145-153 - Pankaj Gupta, Hinrich Schütze:
LISA: Explaining Recurrent Neural Network Judgments via Layer-wIse Semantic Accumulation and Example to Pattern Transformation. 154-164 - Dieuwke Hupkes, Sanne Bouwmeester, Raquel Fernández:
Analysing the potential of seq-to-seq models for incremental interpretation in task-oriented dialogue. 165-174 - Felix Stahlberg, Danielle Saunders, Bill Byrne:
An Operation Sequence Model for Explainable Neural Machine Translation. 175-186 - Andreas Krug, Sebastian Stober:
Introspection for convolutional automatic speech recognition. 187-199 - Valentin Trifonov, Octavian-Eugen Ganea, Anna Potapenko, Thomas Hofmann:
Learning and Evaluating Sparse Interpretable Sentence Embeddings. 200-210 - Ethan Wilcox, Roger Levy, Takashi Morita, Richard Futrell:
What do RNN Language Models Learn about Filler-Gap Dependencies? 211-221 - Jaap Jumelet, Dieuwke Hupkes:
Do Language Models Understand Anything? On the Ability of LSTMs to Understand Negative Polarity Items. 222-231 - Natalia Skachkova, Thomas Alexander Trost, Dietrich Klakow:
Closing Brackets with Recurrent Neural Networks. 232-239 - Mario Giulianelli, Jack Harding, Florian Mohnert, Dieuwke Hupkes, Willem H. Zuidema:
Under the Hood: Using Diagnostic Classifiers to Investigate and Improve how Language Models Track Agreement Information. 240-248 - Martin Tutek, Jan Snajder:
Iterative Recursive Attention Model for Interpretable Sequence Classification. 249-257 - Avery Hiebert, Cole Peterson, Alona Fyshe, Nishant A. Mehta:
Interpreting Word-Level Hidden State Behaviour of Character-Level LSTM Language Models. 258-266 - Gaël Letarte, Frédérik Paradis, Philippe Giguère, François Laviolette:
Importance of Self-Attention for Sentiment Analysis. 267-275 - Pia Sommerauer, Antske Fokkens:
Firearms and Tigers are Dangerous, Kitchen Knives and Zebras are Not: Testing whether Word Embeddings Can Tell. 276-286 - Alessandro Raganato, Jörg Tiedemann:
An Analysis of Encoder Representations in Transformer-Based Machine Translation. 287-297 - Johnny Wei, Khiem Pham, Brendan O'Connor, Brian Dillon:
Evaluating Grammaticality in Seq2seq Models with a Broad Coverage HPSG Grammar: A Case Study on Machine Translation. 298-305 - Yiding Hao, William Merrill, Dana Angluin, Robert Frank, Noah Amsel, Andrew Benz, Simon Mendelsohn:
Context-Free Transductions with Neural Stacks. 306-315 - David Harbecke, Robert Schwarzenberg, Christoph Alt:
Learning Explanations from Language Data. 316-318 - Barbara Rychalska, Dominika Basaj, Anna Wróblewska, Przemyslaw Biecek:
How much should you ask? On the question structure in QA systems. 319-321 - Barbara Rychalska, Dominika Basaj, Anna Wróblewska, Przemyslaw Biecek:
Does it care what you asked? Understanding Importance of Verbs in Deep Learning QA System. 322-324 - Nina Pörner, Benjamin Roth, Hinrich Schütze:
Interpretable Textual Neuron Representations for NLP. 325-327 - Naomi Saphra, Adam Lopez:
Language Models Learn POS First. 328-330 - Nicolas Garneau, Jean-Samuel Leboeuf, Luc Lamontagne:
Predicting and interpreting embeddings for out of vocabulary words in downstream tasks. 331-333 - Geoff Bacon, Terry Regier:
Probing sentence embeddings for structure-dependent tense. 334-336 - Adam Poliak, Aparajita Haldar, Rachel Rudinger, J. Edward Hu, Ellie Pavlick, Aaron Steven White, Benjamin Van Durme:
Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation. 337-340 - Kyoungrok Jang, Sung-Hyon Myaeng, Sang-Bum Kim:
Interpretable Word Embedding Contextualization. 341-343 - Lyan Verwimp, Hugo Van hamme, Vincent Renkens, Patrick Wambacq:
State Gradients for RNN Memory Analysis. 344-346 - David Marecek, Rudolf Rosa:
Extracting Syntactic Trees from Transformer Encoder Self-Attentions. 347-349 - Thomas Lippincott:
Portable, layer-wise task performance monitoring for NLP models. 350-352 - Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, Samuel R. Bowman:
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding. 353-355 - Clara Vania, Adam Lopez:
Explicitly modeling case improves neural dependency parsing. 356-358 - Kelly W. Zhang, Samuel R. Bowman:
Language Modeling Teaches You More than Translation Does: Lessons Learned Through Auxiliary Syntactic Task Analysis. 359-361 - Steven Derby, Paul Miller, Brian Murphy, Barry Devereux:
Representation of Word Meaning in the Intermediate Projection Layer of a Neural Language Model. 362-364 - Ben Peters, Vlad Niculae, André F. T. Martins:
Interpretable Structure Induction via Sparse Attention. 365-367 - Hendrik Strobelt, Sebastian Gehrmann, Michael Behrisch, Adam Perer, Hanspeter Pfister, Alexander M. Rush:
Debugging Sequence-to-Sequence Models with Seq2Seq-Vis. 368-370 - Phu Mon Htut, Kyunghyun Cho, Samuel R. Bowman:
Grammar Induction with Neural Language Models: An Unusual Replication. 371-373 - Prajit Dhar, Arianna Bisazza:
Does Syntactic Knowledge in Multilingual Language Models Transfer Across Languages? 374-377 - Kaylee Burns, Aida Nematzadeh, Erin Grant, Alison Gopnik, Thomas L. Griffiths:
Exploiting Attention to Reveal Shortcomings in Memory Models. 378-380 - Pranava Swaroop Madhyastha, Josiah Wang, Lucia Specia:
End-to-end Image Captioning Exploits Distributional Similarity in Multimodal Space. 381-383 - Denis Paperno:
Limitations in learning an interpreted language with recurrent models. 384-386
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